Click-through　rate　(CTR)　prediction　aims　to　estimate　the　probability　of　a　user　clicking　on　a　particular　item,　making　it　one　of　the　core　tasks　in　various　recommendation　platforms.　In　such　systems,　user　behavior　data　are　crucial　for　capturing　user　interests,　which　has　garnered　significant　attention　from　both　academia　and　industry,　leading　to　the　development　of　various　user　behavior　modeling　methods.　However,　existing　models　still　face　unresolved　issues,　as　they　fail　to　capture　the　complex　diversity　of　user　interests　at　the　semantic　level,　refine　user　interests　effectively,　and　uncover　users’　potential　interests.　To　address　these　challenges,　we　propose　a　novel　model　called　knowledge-enhanced　Interest　segment　division　attention　network　(KISDAN),　which　can　effectively　and　comprehensively　model　user　interests.　Specifically,　to　leverage　the　semantic　information　within　user　behavior　sequences,　we　employ　the　structure　of　a　knowledge　graph　to　divide　user　behavior　sequence　into　multiple　interest　segments.　To　provide　a　comprehensive　representation　of　user　interests,　we　further　categorize　user　interests　into　strong　and　weak　interests.　By　leveraging　both　the　knowledge　graph　and　the　item　co-occurrence　graph,　we　explore　users’　potential　interests　from　two　perspectives.　This　methodology　allows　KISDAN　to　better　understand　the　diversity　of　user　interests.　Finally,　we　extensively　evaluate　KISDAN　on　three　benchmark　datasets,　and　the　experimental　results　consistently　demonstrate　that　the　KISDAN　model　outperforms　state-of-the-art　models　across　various　evaluation　metrics,　which　validates　the　effectiveness　and　superiority　of　KISDAN.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2024.